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Duration 21 hours
Course Outline
Introduction to AI-Augmented SQL
- Overview of AI integration within data systems.
- The shift from traditional SQL to AI-assisted querying.
- Key enterprise use cases and associated benefits.
Understanding LLMs in SQL Context
- How LLMs interpret and generate structured queries.
- Comparative analysis of GPT, LlaMA, DeepSeek, Qwen, and Mistral for SQL applications.
- Fine-tuning models for effective database interaction.
Natural Language to SQL (NL2SQL) Systems
- Architectures and methodologies for NL2SQL.
- Building and deploying text-to-SQL pipelines.
- Evaluating query accuracy and understanding user intent.
AI-Assisted Query Optimization
- Utilizing AI to identify and correct inefficient queries.
- LLM-based query rewriting to enhance performance.
- Integrating AI optimization into PostgreSQL and SQL Server.
Security, Governance, and Auditability
- Managing access controls for AI-generated queries.
- Ensuring explainability and regulatory compliance.
- Implementing AI governance within enterprise data systems.
LLM Integration and Orchestration
- Connecting SQL engines with AI APIs.
- Utilizing frameworks such as LangChain and LlamaIndex.
- Deploying AI components across hybrid and cloud architectures.
Practical Implementation Labs
- Establishing AI-SQL connections and test environments.
- Creating and evaluating AI-generated queries.
- Measuring performance gains through AI optimization.
Future Trends and Enterprise Adoption Strategies
- The rise of AI-native database systems and SQL evolution.
- Integration with data lakes, BI tools, and data pipelines.
- Building internal AI query assistants for organizations.
Requirements
- A solid grasp of SQL fundamentals.
- Practical experience in database administration or data engineering.
- Foundational knowledge of AI or machine learning concepts.
Target Audience
- Data engineers and database administrators.
- Enterprise architects and analytics leads.
- AI integration and platform engineering teams.